This application proposes development of new computer algorithms for reasoning about time-varying data from underlying causal models. The signals consist of two or more channels of events, each event having several properties including time of occurrence and event shape class. The models can be represented as graphs in which the nodes are digital components, some of which give rise to observable events. The arcs are physical connections associated with time delays. The application domain is the electrocardiogram, in which the P waves and QRS complexes represent two event channels, and optional intracardiac recordings contribute additional channels. The P waves and QRS complexes are the results of the all-or-nothing depolarization of the atria and ventricles, respectively. These cardiac structures are connected anatomically and functionally by a series of other structures, each associated with a characteristic conduction time. The analytic approach will be a variation of the hypothesize-and-test paradigm. A hierarchy of models will be constructed whose base is the most complex, most comprehensive model, and whose derivatives are all simpler variants. The control loop will be based on the "tracking" concept used in many knowledge-based monitoring systems. The output of the proposed system will be one or more complete causal explanations of the observed signal. Each explanation will consist of an instantiated model and event-by-event annotation of causality based on the model. When a signal admits of more than one explanatory model, each will be developed an displayed separately. The hypothesis to be tested is that a cardiac arrhythmia monitor constructed using knowledge-based programming methods will perform substantially better than current clinical arrhythmia monitors as measured by correct diagnoses of clinically important arrhythmias such as second degree atrioventricular block, wide complex tachycardias, and detection of subtle signs of preexcitation. This project is attractive for two reasons. First, it offers new knowledge-based algorithms for model-based reasoning about time-varying signals. This is an unsolved problem in the expert systems field. Second, current cardiac arrhythmia monitors do not perform nearly as well as do expert nurses, technicians, and physicians. The proposed project will contribute to the development of an improved generation of models that should result in improved care of patients in intensive care settings.